Function to compute the analysis of variance of ILS data, taking into account the laboratories and material factors.
Usage
lab.aov(x, ...)
# S3 method for default
lab.aov(
x,
var.index = 1,
replicate.index = 2,
material.index = 3,
laboratory.index = 4,
data.name = NULL,
level = 0.95,
plot = FALSE,
pages = 0,
...
)
# S3 method for lab.qcdata
lab.aov(x, level = 0.95, plot = FALSE, pages = 0, ...)
Arguments
- x
An object of class
lab.qcdata
(Univariate Quality Control Data).- ...
Other arguments passed to or from methods.
- var.index
A scalar with the column number corresponding to the observed variable (the critical to quality variable). Alternativelly can be a string with the name of the quality variable.
- replicate.index
A scalar with the column number corresponding to the index each replicate.
- material.index
A scalar corresponding to the replicated number.
- laboratory.index
A scalar that defines the index number of each laboratory.
- data.name
A string specifying the name of the variable which appears on the plots. If name is not provided, it is taken from the object given as data.
- level
Requested confidence level (0.95 by default).
- plot
If TRUE, confidence intervals are plot.
- pages
By default 0, it indicates the number of pages over which to spread the output. For example, if pages=1, all terms will be plotted on one page with the layout performed automatically. If pages=0, one plot will be displayed by each tested material.
References
WHothorn T., Bretz, F., and Westfall, P. (2008), Simultaneous inference in general parametric models. Biometrical Journal, 50(3):346-363.
Heyden, Y., Smeyers-Verbeke, J. (2007), Set-up and evaluation of interlaboratory studies. J. Chromatogr. A, 1158:158-167.
Examples
if (FALSE) {
library(ILS)
data(Glucose)
Glucose.qcdata <- lab.qcdata(Glucose)
str(Glucose.qcdata)
lab.aov(Glucose.qcdata,level = 0.95, plot = TRUE, pages = 1)
}